Abstract
Artificial intelligence (AI) techniques are becoming more and more widespread. This is directly related to technology progress and aspects as the flexibility and adaptability of the algorithms considered, key characteristics that allow their use in the most variegated fields. Precisely the increasing diffusion of these techniques leads to the necessity of evaluating their robustness and reliability. This field is still quite unexplored, especially considering the automotive sector, where the algorithms need to be prepared to answer noise problems in data acquisition. For this reason, a methodology directly linked to previous works in the heavy vehicles field is presented. In particular, the same is focused on the estimation of rollover indexes, one of the main issues in road safety scenarios. The purpose is to expand the cited works, addressing the LSTM networks performance in case of strongly disturbed signals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldi, M.M., Perboli, G., Tadei, R.: Driver maneuvers inference through machine learning. In: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (eds.) MOD 2016. LNCS (LNAI and LNB), vol. 10122, pp. 182–192. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51469-7_15
Chen, X., Chen, W., Hou, L., Hu, H., Bu, X., Zhu, Q.: A novel data-driven rollover risk assessment for articulated steering vehicles using RNN. J. Mech. Sci. Technol. 34(5), 2161–2170 (2020). https://doi.org/10.1007/s12206-020-0437-4
Imine, H., Benallegue, A., Madani, T., Srairi, S.: Rollover risk prediction of heavy vehicle using high-order sliding-mode observer: experimental results. IEEE Trans. Veh. Technol. 63(6), 2533–2543 (2014). https://doi.org/10.1109/TVT.2013.2292998
Le, X.H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7) (2019). https://doi.org/10.3390/w11071387
Lenkutis, T., Čerškus, A., Šešok, N., Dzedzickis, A., Bučinskas, V.: Road surface profile synthesis: assessment of suitability for simulation. Symmetry 13(1), 1–14 (2021). https://doi.org/10.3390/sym13010068
Lindemann, B., Müller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long short-term memory networks for time series prediction. Procedia CIRP 99, 650–655 (2021). https://doi.org/10.1016/j.procir.2021.03.088
Liu, Y., Cui, D.: Collaborative model analysis on ride comfort and handling stability. J. Vibroeng. 21(6), 1724–1737 (2019). https://doi.org/10.21595/jve.2019.20454
Perboli, G., Arabnezhad, E.: A Machine Learning-based DSS for mid and long-term company crisis prediction. Expert Syst. Appl. 114758 (2021). https://doi.org/10.1016/j.eswa.2021.114758
Perboli, G., Tronzano, A., Rosano, M., Tarantino, L., Velardocchia, F.: Using machine learning to assess public policies: a real case study for supporting SMEs development in Italy. In: 2021 IEEE Technology & Engineering Management Conference - Europe (TEMSCON-EUR), pp. 1–6. IEEE (2021). https://doi.org/10.1109/TEMSCON-EUR52034.2021.9488581
Sellami, Y., Imine, H., Boubezoul, A., Cadiou, J.C.: Rollover risk prediction of heavy vehicles by reliability index and empirical modelling. Veh. Syst. Dyn. 56(3), 385–405 (2018). https://doi.org/10.1080/00423114.2017.1381980
Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: a common framework to provide explanations and analyse the fairness and robustness of black-box models. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES 2020, pp. 166–172. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3375627.3375812
Tota, A., Dimauro, L., Velardocchia, F., Paciullo, G., Velardocchia, M.: An intelligent predictive algorithm for the anti-rollover prevention of heavy vehicles for off-road applications. Machines 10, 835 (2022). https://doi.org/10.3390/machines10100835
Us Department of Transportation: Traffic safety facts 2016: a compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Technical report, NHTSA (2017)
Velardocchia, F., Perboli, G., Vigliani, A.: Analysis of heavy vehicles rollover with artificial intelligence techniques. In: Nicosia, G., et al. (eds.) LOD 2022. LNCS (LNAI and LNB), vol. 13810, pp. 294–308. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25599-1_22
Zhu, T., Yin, X., Li, B., Ma, W.: A reliability approach to development of rollover prediction for heavy vehicles based on SVM empirical model with multiple observed variables. IEEE Access 8, 89367–89380 (2020). https://doi.org/10.1109/ACCESS.2020.2994026
Acknowledgements
While working on this article, Guido Perboli was the Head of the Urban Mobility and Logistics Systems (UMLS) initiative of the interdepartmental Center for Automotive Research and Sustainable mobility (CARS) at the Politecnico di Torino. Partial funds for the project were given under the Italian ”PNRR project, DM 1061”. Prof. Maria Elena Bruni acknowledges financial support from: PNRR MUR project PE0000013-FAIR.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bruni, M.E., Perboli, G., Velardocchia, F. (2024). LSTM Noise Robustness: A Case Study for Heavy Vehicles. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham. https://doi.org/10.1007/978-3-031-53966-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-53966-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53965-7
Online ISBN: 978-3-031-53966-4
eBook Packages: Computer ScienceComputer Science (R0)